From Single Image Query to Detailed 3D Reconstruction
Structure-from-Motion for unordered image collections has significantly advanced in scale over the last decade. This impressive progress can be in part attributed to the introduction of efficient retrieval methods for those systems. While this boosts scalability, it also limits the amount of detail that the large-scale reconstruction systems are able to produce. In this paper, we propose a joint reconstruction and retrieval system that maintains the scalability of large-scale Structure-from-Motion systems while also recovering the often lost ability of reconstructing fine details of the scene. We demonstrate our proposed method on a large-scale dataset of 7.4 million images downloaded from the Internet.
Code (0)
등록된 구현이 없습니다.
Tasks
3D ReconstructionRetrievalSimilar Papers 제목 키워드 기반
FlexAvatar: Flexible Large Reconstruction Model for Animatable Gaussian Head Avatars with Detailed Deformation
We present FlexAvatar, a flexible large reconstruction model for high-fidelity 3D head avatars with detailed dynamic deformation from single or sparse images, without requiring camera poses or expression labels. It lever…
Learning Detailed Face Reconstruction from a Single Image
Reconstructing the detailed geometric structure of a face from a given image is a key to many computer vision and graphics applications, such as motion capture and reenactment. The reconstruction task is challenging as h…
Face ReconstructionObject RecognitionDeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image
3D reconstruction from a single image is a key problem in multiple applications ranging from robotic manipulation to augmented reality. Prior methods have tackled this problem through generative models which predict 3D r…
3D Reconstruction3D Shape ReconstructionFormRetrievalA Hierarchical Representation Network for Accurate and Detailed Face Reconstruction from In-The-Wild Images
Limited by the nature of the low-dimensional representational capacity of 3DMM, most of the 3DMM-based face reconstruction (FR) methods fail to recover high-frequency facial details, such as wrinkles, dimples, etc. Some …
3D Face ReconstructionDisentanglementFace ReconstructionCNN-based Real-time Dense Face Reconstruction with Inverse-rendered Photo-realistic Face Images
With the powerfulness of convolution neural networks (CNN), CNN based face reconstruction has recently shown promising performance in reconstructing detailed face shape from 2D face images. The success of CNN-based metho…
3D Face ReconstructionFace ModelFace ReconstructionInverse Rendering